A meta-study (or meta-analysis) collects and analyzes many studies on the same topic to understand if there is a meaningful, overall result. Meta-studies can support (or refute) interventions, spur new investigations, and lead to novel clinical guidelines. However, constructing meta-studies is a time intensive process of searching the literature, compiling the results, and performing the statistical analysis. Due to the time commitment that is required, many topics are unexplored, and many meta-studies are not kept up-to-date with the latest published results. Finally, a number of (unknown) biases, via subjective choices during the meta-study, may influence the results. Our long-term goal is to automate, as much as possible, the meta-study process. This should decrease subjective bias; increase the dissemination of evidence, especially for diseases and interventions that receive less attention; and allow for the automatic updating of meta-studies as new results are published. We propose a computer system that uses statistical machine learning to gather and group studies focused on similar interventions and outcomes; extract the necessary results from the text; and analyze the results using standard meta-analysis techniques. The final output will be presented in a spreadsheet-like Web-interface where users can explore and even change the data and meta-analyses. Our team uniquely blends technical expertise in machine learning with leadership in publishing meta-studies about Inflammatory Bowel Disease (IBD), our disease of focus for our Phase I feasibility study. We are therefore qualified technically and able to ensure that the techniques generate valid and accurate meta-studies. Our Phase I results will define the current state-of-the-art for this novel task. Further, although we will initially focus n IBD, our Phase I results will demonstrate that our approach can generalize to other diseases, eventually applying to any intervention and any disease. The feasibility shown by our Phase I results will motivate our Phase II effort where we will focus on dramatically improving the approach, yielding broad coverage of all medical literature and generating human-quality meta-studies. We note that by the end of Phase I we should have a viable end-to-end prototype, focused on IBD, which we can begin taking to market. The final product should significantly benefit our target markets given the Phase II emphasis to improve the technology, user experience, and scope of covered diseases.